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license: apache-2.0
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```
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---
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license: apache-2.0
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language:
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- zh
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- en
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---
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<div align="center">
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<img src="https://github.com/OpenBMB/MiniCPM/tree/main/assets/minicpm_logo.png" width="500em" ></img>
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</div>
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<p align="center">
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<a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">MiniCPM Repo</a> |
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<a href="https://arxiv.org/abs/2404.06395" target="_blank">MiniCPM Paper</a> |
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<a href="https://github.com/OpenBMB/MiniCPM-V/" target="_blank">MiniCPM-V Repo</a> |
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Join us in <a href="https://discord.gg/3cGQn9b3YM" target="_blank">Discord</a> and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">WeChat</a>
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</p>
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## Introduction
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MiniCPM3-4B is the 3rd generation of MiniCPM series. The overall performance of MiniCPM3-4B surpasses Phi-3.5-mini-Instruct and GPT-3.5-Turbo-0125, being comparable with many recent 7B~9B models.
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Compared to MiniCPM1.0/MiniCPM2.0, MiniCPM3-4B has a more powerful and versatile skill set to enable more general usage. MiniCPM3-4B supports function call, along with code interpreter. Please refer to [Advanced Features](https://github.com/zh-zheng/minicpm?tab=readme-ov-file#%E8%BF%9B%E9%98%B6%E5%8A%9F%E8%83%BD) for usage guidelines.
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MiniCPM3-4B has a 32k context window. Equipped with LLMxMapReduce, MiniCPM3-4B can handle infinite context theoretically, without requiring huge amount of memory.
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## Usage
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### Inference with Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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path = "openbmb/MiniCPM3-4B"
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device = "cuda"
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tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)
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messages = [
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{"role": "user", "content": "推荐5个北京的景点。"},
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]
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model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device)
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model_outputs = model.generate(
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model_inputs,
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max_new_tokens=1024,
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top_p=0.7,
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temperature=0.7,
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repetition_penalty=1.02
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)
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output_token_ids = [
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model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs))
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]
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responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
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print(responses)
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```
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### Inference with [vLLM](https://github.com/vllm-project/vllm)
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```python
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from transformers import AutoTokenizer
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from vllm import LLM, SamplingParams
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model_name = "openbmb/MiniCPM3-4B"
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prompt = [{"role": "user", "content": "推荐5个北京的景点。"}]
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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input_text = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)
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llm = LLM(
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model=model_name,
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trust_remote_code=True,
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tensor_parallel_size=1
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)
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sampling_params = SamplingParams(top_p=0.7, temperature=0.7, max_tokens=1024, repetition_penalty=1.02)
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outputs = llm.generate(prompts=input_text, sampling_params=sampling_params)
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print(outputs[0].outputs[0].text)
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```
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## Evaluation Results
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<table>
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<tr>
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<td>Benchmark</td>
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<td>Qwen2-7B-Instruct</td>
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<td>GLM-4-9B-Chat</td>
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<td>Gemma2-9B-it</td>
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<td>Llama3.1-8B-Instruct</td>
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<td>GPT-3.5-Turbo-0125</td>
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<td>Phi-3.5-mini-Instruct(3.8B)</td>
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<td>MiniCPM3-4B </td>
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</tr>
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<tr>
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<td colspan="15" align="left"><strong>English</strong></td>
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</tr>
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<td>MMLU</td>
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<td>70.5</td>
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<td>72.4</td>
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<td>72.6</td>
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<td>69.4</td>
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<td>69.2</td>
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<td>68.4</td>
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<td>67.2 </td>
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</tr>
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<tr>
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<td>BBH</td>
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<td>64.9</td>
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<td>76.3</td>
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<td>65.2</td>
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<td>67.8</td>
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<td>70.3</td>
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<td>68.6</td>
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<td>70.2 </td>
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</tr>
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<td>MT-Bench</td>
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<td>8.41</td>
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<td>8.35</td>
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<td>7.88</td>
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<td>8.28</td>
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<td>8.17</td>
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<td>8.60</td>
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<td>8.41 </td>
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</tr>
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<tr>
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<td>IFEVAL (Prompt Strict-Acc.)</td>
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<td>51.0</td>
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<td>64.5</td>
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<td>71.9</td>
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<td>71.5</td>
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<td>58.8</td>
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<td>49.4</td>
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<td>68.4 </td>
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</tr>
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<td colspan="15" align="left"><strong>Chinese</strong></td>
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</tr>
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<td>CMMLU</td>
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<td>80.9</td>
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<td>71.5</td>
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<td>59.5</td>
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<td>55.8</td>
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<td>54.5</td>
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<td>46.9</td>
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<td>73.3 </td>
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</tr>
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<tr>
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<td>CEVAL</td>
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<td>77.2</td>
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<td>75.6</td>
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<td>56.7</td>
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<td>55.2</td>
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<td>52.8</td>
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<td>46.1</td>
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<td>73.6 </td>
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</tr>
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<tr>
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<td>AlignBench v1.1</td>
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<td>7.10</td>
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<td>6.61</td>
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<td>7.10</td>
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<td>5.68</td>
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<td>5.82</td>
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<td>5.73</td>
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<td>6.74 </td>
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</tr>
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<tr>
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<td>FollowBench-zh (SSR)</td>
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<td>63.0</td>
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<td>56.4</td>
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<td>57.0</td>
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<td>50.6</td>
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<td>64.6</td>
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<td>58.1</td>
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<td>66.8 </td>
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</tr>
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<tr>
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<td colspan="15" align="left"><strong>Math</strong></td>
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</tr>
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<tr>
|
| 185 |
+
<td>MATH</td>
|
| 186 |
+
<td>49.6</td>
|
| 187 |
+
<td>50.6</td>
|
| 188 |
+
<td>46.0</td>
|
| 189 |
+
<td>51.9</td>
|
| 190 |
+
<td>41.8</td>
|
| 191 |
+
<td>46.4</td>
|
| 192 |
+
<td>46.6 </td>
|
| 193 |
+
</tr>
|
| 194 |
+
<tr>
|
| 195 |
+
<td>GSM8K</td>
|
| 196 |
+
<td>82.3</td>
|
| 197 |
+
<td>79.6</td>
|
| 198 |
+
<td>79.7</td>
|
| 199 |
+
<td>84.5</td>
|
| 200 |
+
<td>76.4</td>
|
| 201 |
+
<td>82.7</td>
|
| 202 |
+
<td>81.1 </td>
|
| 203 |
+
</tr>
|
| 204 |
+
<tr>
|
| 205 |
+
<td>MathBench</td>
|
| 206 |
+
<td>63.4</td>
|
| 207 |
+
<td>59.4</td>
|
| 208 |
+
<td>45.8</td>
|
| 209 |
+
<td>54.3</td>
|
| 210 |
+
<td>48.9</td>
|
| 211 |
+
<td>54.9</td>
|
| 212 |
+
<td>65.6 </td>
|
| 213 |
+
</tr>
|
| 214 |
+
<tr>
|
| 215 |
+
<td colspan="15" align="left"><strong>Code</strong></td>
|
| 216 |
+
</tr>
|
| 217 |
+
<tr>
|
| 218 |
+
<td>HumanEval+</td>
|
| 219 |
+
<td>70.1</td>
|
| 220 |
+
<td>67.1</td>
|
| 221 |
+
<td>61.6</td>
|
| 222 |
+
<td>62.8</td>
|
| 223 |
+
<td>66.5</td>
|
| 224 |
+
<td>68.9</td>
|
| 225 |
+
<td>68.3 </td>
|
| 226 |
+
</tr>
|
| 227 |
+
<tr>
|
| 228 |
+
<td>MBPP+</td>
|
| 229 |
+
<td>57.1</td>
|
| 230 |
+
<td>62.2</td>
|
| 231 |
+
<td>64.3</td>
|
| 232 |
+
<td>55.3</td>
|
| 233 |
+
<td>71.4</td>
|
| 234 |
+
<td>55.8</td>
|
| 235 |
+
<td>63.2 </td>
|
| 236 |
+
</tr>
|
| 237 |
+
<tr>
|
| 238 |
+
<td>LiveCodeBench</td>
|
| 239 |
+
<td>22.2</td>
|
| 240 |
+
<td>20.2</td>
|
| 241 |
+
<td>19.2</td>
|
| 242 |
+
<td>20.4</td>
|
| 243 |
+
<td>24.0</td>
|
| 244 |
+
<td>19.6</td>
|
| 245 |
+
<td>22.6 </td>
|
| 246 |
+
</tr>
|
| 247 |
+
<tr>
|
| 248 |
+
<td colspan="15" align="left"><strong>Function Call</strong></td>
|
| 249 |
+
</tr>
|
| 250 |
+
<tr>
|
| 251 |
+
<td>BFCL</td>
|
| 252 |
+
<td>71.6</td>
|
| 253 |
+
<td>70.1</td>
|
| 254 |
+
<td>19.2</td>
|
| 255 |
+
<td>73.3</td>
|
| 256 |
+
<td>75.4</td>
|
| 257 |
+
<td>48.4</td>
|
| 258 |
+
<td>76.0 </td>
|
| 259 |
+
</tr>
|
| 260 |
+
<tr>
|
| 261 |
+
<td colspan="15" align="left"><strong>Overall</strong></td>
|
| 262 |
+
</tr>
|
| 263 |
+
<tr>
|
| 264 |
+
<td>Average</td>
|
| 265 |
+
<td>65.3</td>
|
| 266 |
+
<td>65.0</td>
|
| 267 |
+
<td>57.9</td>
|
| 268 |
+
<td>60.8</td>
|
| 269 |
+
<td>61.0</td>
|
| 270 |
+
<td>57.2</td>
|
| 271 |
+
<td><strong>66.3</strong></td>
|
| 272 |
+
</tr>
|
| 273 |
+
</table>
|
| 274 |
+
|
| 275 |
+
|
| 276 |
+
## Statement
|
| 277 |
+
* As a language model, MiniCPM3-4B generates content by learning from a vast amount of text.
|
| 278 |
+
* However, it does not possess the ability to comprehend or express personal opinions or value judgments.
|
| 279 |
+
* Any content generated by MiniCPM3-4B does not represent the viewpoints or positions of the model developers.
|
| 280 |
+
* Therefore, when using content generated by MiniCPM3-4B, users should take full responsibility for evaluating and verifying it on their own.
|
| 281 |
+
|
| 282 |
+
## LICENSE
|
| 283 |
+
* This repository is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.
|
| 284 |
+
* The usage of MiniCPM3-4B model weights must strictly follow [MiniCPM Model License.md](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md).
|
| 285 |
+
* The models and weights of MiniCPM3-4B are completely free for academic research. after filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, are also available for free commercial use.
|
| 286 |
+
|
| 287 |
+
## Citation
|
| 288 |
+
|
| 289 |
+
```
|
| 290 |
+
@article{hu2024minicpm,
|
| 291 |
+
title={MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies},
|
| 292 |
+
author={Hu, Shengding and Tu, Yuge and Han, Xu and He, Chaoqun and Cui, Ganqu and Long, Xiang and Zheng, Zhi and Fang, Yewei and Huang, Yuxiang and Zhao, Weilin and others},
|
| 293 |
+
journal={arXiv preprint arXiv:2404.06395},
|
| 294 |
+
year={2024}
|
| 295 |
+
}
|
| 296 |
```
|